Weed Management in Kidney Bean with Tank Mixes of S-Metolachlor, Imazethapyr and Linuron
Bibliographic record
Abstract
Field studies were conducted in various locations in Ontario during 2011 to 2013 to evaluate s-metolachlor, imazethapyr and linuron applied preemergence (PRE) alone and in tankmix combination for the control of troublesome weeds in kidney bean. S-metolachlor, imazethapyr, linuron, s-metolachlor + imazethapyr, s-metolachlor + linuron and s-metolachlor + imazethapyr + linuron applied PRE at rates evaluated caused 3% or less injury in kidney bean. S-metolachlor provided 87% - 91% control of redroot pigweed, 46% - 55% control of common lambsquarters, and 96% - 97% control of green foxtail. Imazethapyr provided 93% - 96% control of redroot pigweed, 96% - 99% control of lambsquarters and 86% - 93% control of green foxtail. Linuron provided 82% - 98% control of lambsquarters, 82% - 99% control of redroot pigweed and 55% - 85% control of green foxtail. The tank mixes of s-metolachlor plus imazethapyr, s-metolachlor plus linuron, and s-metolachlor plus imazethapyr plus linuron provided 92% - 100% control of lambsquarters, redroot pigweed and green foxtail. Generally, kidney bean yields reflected the level of weed control. Based on these results, tank mixes of s-metolachlor plus imazethapyr, s-metolachlor plus linuron, and s-metolachlor plus imazethapyr plus linuron all provide an adequate margin of crop safety and excellent control of redroot pigweed, common lambsquarters and green foxtail in kidney bean.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".